Lei Hu

Extreme meteorological phenomena—characterized by gale-force winds, torrential rainfall, and ice-snow accumulation—pose significant threats to the operational safety of high-speed railways, with wind-induced hazards being especially critical. Such events can trigger catastrophic incidents, including train derailments and service disruptions, as evidenced by numerous documented cases worldwide. To bolster the wind resilience of high-speed railway systems, high-precision wind speed prediction has become a cornerstone for ensuring operational safety. This research presents a systematic review of international advancements in railway wind early warning systems, critically evaluating the technical attributes and performance constraints of four primary paradigms: physical numerical models, statistical methods, machine learning algorithms, and hybrid frameworks. Moving beyond a simple taxonomy, this paper delineates the strengths, limitations, and domain-specific applicability of each approach within the high-speed railways context. Furthermore, it assesses the transformative potential of emerging large-scale Artificial Intelligence (AI) meteorological models for wind speed forecasting. A quantitative comparison is provided to facilitate rigorous methodological assessment. The findings reveal four critical technical bottlenecks: (1) low computational efficiency of numerical models; (2) insufficient spatiotemporal resolution of monitoring data; (3) poor generalization of predictive models; and (4) the “black-box” nature and weak interpretability of AI models. To address these, this paper posits that future research should prioritize key technologies including multi-source heterogeneous data fusion, algorithmic optimization, design of intelligent algorithms, probabilistic risk forecasting, and the synergistic integration of AI with numerical weather prediction (NWP). Such advancements will catalyze the development of more robust HSR wind warning systems, ensuring sustained safety and operational efficiency under volatile meteorological conditions.

Real-time semantic segmentation in railway safety monitoring presents significant computational challenges for edge-deployed vision systems, where accurate object detection directly impacts operational safety under strict resource constraints. Existing approaches struggle to achieve optimal trade-offs between segmentation accuracy and computational efficiency while maintaining robustness across varying environmental conditions. To address these challenges, we propose HybridSeg, an efficient multi-scale vision architecture that integrates Mamba-based sequence modeling with hierarchical feature fusion for resource-constrained railway monitoring applications. Our framework incorporates four key technical innovations: (1) Structure-Aware Preprocessing (SAP) that enhances input features through multi-scale structural analysis, (2) Structure-Aware Deformable Mamba (SADM) blocks enabling efficient long-range dependency capture via multi-directional scanning and deformable spatial attention, (3) Multi-Scale Feature Fusion (MSFF) with cross-scale attention for hierarchical feature integration, and (4) Cross-Scale Consistency (CSC) training that enforces multi-scale feature alignment. The decoder employs gated skip connections for adaptive feature combination across resolution levels. A comprehensive evaluation of railway surveillance datasets demonstrates superior performance, achieving 84.8±0.004% mIoU and 93.8±0.003% pixel accuracy while maintaining real-time efficiency at 31.8 FPS with only 38.9M parameters. The architecture exhibits robust performance across diverse environmental conditions, showing merely 4.1 percentage points of degradation under challenging nighttime scenarios. Our method effectively segments three critical object classes — personnel, foreign objects, and railway infrastructure — delivering substantial improvements in safety-critical detection tasks while enabling practical deployment on resource-limited edge computing platforms.